{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 327,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from keras import applications\n",
    "from keras.models import Sequential\n",
    "from keras.models import Model\n",
    "from keras.layers import Dropout, Flatten, Dense, Activation\n",
    "from keras.callbacks import CSVLogger\n",
    "import tensorflow as tf\n",
    "from scipy.ndimage import imread\n",
    "import numpy as np\n",
    "import random\n",
    "from keras.layers import LSTM\n",
    "from keras.layers import Conv1D, MaxPooling1D, LeakyReLU\n",
    "from keras import backend as K\n",
    "import keras\n",
    "from keras.callbacks import CSVLogger, ModelCheckpoint\n",
    "from keras.backend.tensorflow_backend import set_session\n",
    "from keras import optimizers\n",
    "import h5py\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "import os\n",
    "import pandas as pd\n",
    "# import matplotlib\n",
    "\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 328,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "os.environ['CUDA_DEVICE_ORDER'] = 'PCI_BUS_ID'\n",
    "os.environ['CUDA_VISIBLE_DEVICES'] = '0'\n",
    "os.environ['TF_CPP_MIN_LOG_LEVEL']='2'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 329,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "config = tf.ConfigProto()\n",
    "config.gpu_options.allow_growth = True\n",
    "set_session(tf.Session(config=config))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 330,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "with h5py.File(''.join(['bitcoin2015to2017_close.h5']), 'r') as hf:\n",
    "    datas = hf['inputs'].value\n",
    "    labels = hf['outputs'].value\n",
    "    input_times = hf['input_times'].value\n",
    "    output_times = hf['output_times'].value\n",
    "    original_inputs = hf['original_inputs'].value\n",
    "    original_outputs = hf['original_outputs'].value\n",
    "    original_datas = hf['original_datas'].value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 331,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "scaler=MinMaxScaler()\n",
    "#split training validation\n",
    "training_size = int(0.8* datas.shape[0])\n",
    "training_datas = datas[:training_size,:,:]\n",
    "training_labels = labels[:training_size,:,:]\n",
    "validation_datas = datas[training_size:,:,:]\n",
    "validation_labels = labels[training_size:,:,:]\n",
    "validation_original_outputs = original_outputs[training_size:,:,:]\n",
    "validation_original_inputs = original_inputs[training_size:,:,:]\n",
    "validation_input_times = input_times[training_size:,:,:]\n",
    "validation_output_times = output_times[training_size:,:,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 332,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(221, 272, 1)"
      ]
     },
     "execution_count": 332,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ground_true = np.append(validation_original_inputs,validation_original_outputs, axis=1)\n",
    "ground_true.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 333,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(221, 272, 1)"
      ]
     },
     "execution_count": 333,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ground_true_times = np.append(validation_input_times,validation_output_times, axis=1)\n",
    "ground_true_times.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 334,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "step_size = datas.shape[1]\n",
    "batch_size= 8\n",
    "nb_features = datas.shape[2]\n",
    "epochs = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 335,
   "metadata": {},
   "outputs": [],
   "source": [
    "#build model\n",
    "\n",
    "model = Sequential()\n",
    "\n",
    "# 2 layers\n",
    "model.add(Conv1D(activation='relu', input_shape=(step_size, nb_features), strides=3, filters=8, kernel_size=20))\n",
    "# model.add(LeakyReLU())\n",
    "model.add(Dropout(0.25))\n",
    "model.add(Conv1D( strides=4, filters=nb_features, kernel_size=16))\n",
    "\n",
    "'''\n",
    "# 3 layers\n",
    "model.add(Conv1D(activation='relu', input_shape=(step_size, nb_features), strides=3, filters=8, kernel_size=8))\n",
    "# model.add(LeakyReLU())\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Conv1D(activation='relu', strides=2, filters=8, kernel_size=8))\n",
    "# model.add(LeakyReLU())\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Conv1D( strides=2, filters=nb_features, kernel_size=8))\n",
    "model.summary()\n",
    "\n",
    "# 4 layers\n",
    "model.add(Conv1D(activation='relu', input_shape=(step_size, nb_features), strides=2, filters=8, kernel_size=2))\n",
    "# model.add(LeakyReLU())\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Conv1D(activation='relu', strides=2, filters=8, kernel_size=2))\n",
    "# model.add(LeakyReLU())\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Conv1D(activation='relu', strides=2, filters=8, kernel_size=2))\n",
    "# model.add(LeakyReLU())\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Conv1D( strides=2, filters=nb_features, kernel_size=2))\n",
    "'''\n",
    "model.load_weights('weights/bitcoin2015to2017_close_CNN_2_relu-44-0.00030.hdf5')\n",
    "model.compile(loss='mse', optimizer='adam')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 336,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 221, 16)\n",
      "(3536,)\n"
     ]
    }
   ],
   "source": [
    "predicted = model.predict(validation_datas)\n",
    "predicted_inverted = []\n",
    "\n",
    "# In[7]:\n",
    "for i in range(original_datas.shape[1]):\n",
    "    scaler.fit(original_datas[:,i].reshape(-1,1))\n",
    "    predicted_inverted.append(scaler.inverse_transform(predicted[:,:,i]))\n",
    "print np.array(predicted_inverted).shape\n",
    "#get only the close data\n",
    "ground_true = ground_true[:,:,0].reshape(-1)\n",
    "ground_true_times = ground_true_times.reshape(-1)\n",
    "ground_true_times = pd.to_datetime(ground_true_times, unit='s')\n",
    "# since we are appending in the first dimension\n",
    "predicted_inverted = np.array(predicted_inverted)[0,:,:].reshape(-1)\n",
    "print np.array(predicted_inverted).shape\n",
    "validation_output_times = pd.to_datetime(validation_output_times.reshape(-1), unit='s')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 337,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>times</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-06-03 16:30:00</td>\n",
       "      <td>2444.999900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-06-03 16:35:00</td>\n",
       "      <td>2440.999999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-06-03 16:40:00</td>\n",
       "      <td>2450.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-06-03 16:45:00</td>\n",
       "      <td>2455.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-06-03 16:50:00</td>\n",
       "      <td>2456.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                times        value\n",
       "0 2017-06-03 16:30:00  2444.999900\n",
       "1 2017-06-03 16:35:00  2440.999999\n",
       "2 2017-06-03 16:40:00  2450.000000\n",
       "3 2017-06-03 16:45:00  2455.000000\n",
       "4 2017-06-03 16:50:00  2456.000000"
      ]
     },
     "execution_count": 337,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ground_true_df = pd.DataFrame()\n",
    "ground_true_df['times'] = ground_true_times\n",
    "ground_true_df['value'] = ground_true\n",
    "ground_true_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 338,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60112, 2)"
      ]
     },
     "execution_count": 338,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ground_true_df = pd.DataFrame()\n",
    "ground_true_df['times'] = ground_true_times\n",
    "ground_true_df['value'] = ground_true\n",
    "ground_true_df.set_index('times').reset_index()\n",
    "ground_true_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 339,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3536, 2)"
      ]
     },
     "execution_count": 339,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction_df = pd.DataFrame()\n",
    "prediction_df['times'] = validation_output_times\n",
    "prediction_df['value'] = predicted_inverted\n",
    "prediction_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 340,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>times</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-06-04 13:50:00</td>\n",
       "      <td>2394.123047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-06-04 13:55:00</td>\n",
       "      <td>2393.950439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-06-04 14:00:00</td>\n",
       "      <td>2399.898926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-06-04 14:05:00</td>\n",
       "      <td>2403.198730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-06-04 14:10:00</td>\n",
       "      <td>2400.576416</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                times        value\n",
       "0 2017-06-04 13:50:00  2394.123047\n",
       "1 2017-06-04 13:55:00  2393.950439\n",
       "2 2017-06-04 14:00:00  2399.898926\n",
       "3 2017-06-04 14:05:00  2403.198730\n",
       "4 2017-06-04 14:10:00  2400.576416"
      ]
     },
     "execution_count": 340,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 341,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# 2016/5\n",
    "prediction_df = prediction_df.loc[(prediction_df[\"times\"].dt.year == 2017 )&(prediction_df[\"times\"].dt.month > 7 ),: ]\n",
    "ground_true_df = ground_true_df.loc[(ground_true_df[\"times\"].dt.year == 2017 )&(ground_true_df[\"times\"].dt.month > 7 ),:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 342,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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7vKU+AiUAAAAAQNoKhluij9sPviTddJPT2LqpwfVNNyWlwXVThdJZe7e79lA6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\nm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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f41cc1fd2d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,10))\n",
    "plt.plot(ground_true_df.times,ground_true_df.value, label = 'Actual')\n",
    "plt.plot(prediction_df.times,prediction_df.value,'ro', label='Predicted')\n",
    "plt.legend(loc='upper left')\n",
    "plt.savefig('result/bitcoin2015to2017_close_CNN_2_relu_result.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 343,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "118021.69414706303"
      ]
     },
     "execution_count": 343,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "mean_squared_error(validation_original_outputs[:,:,0].reshape(-1),predicted_inverted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
